The programme

The challenge is no longer finding another use case. It is scaling the ones that matter.

Financial institutions have accumulated pilots across customer service, fraud, compliance, marketing, operations, software development, risk and employee productivity. But moving from a successful proof of concept to an enterprise capability requires a completely different level of discipline.

Data quality must be addressed. Legacy systems need to connect. Security controls must evolve. Model performance needs continuous monitoring. Employees need new operating models. Governance cannot remain a document sitting outside the technology stack.

FINOVAI 2027 will examine how financial institutions can identify high-value use cases, build the right foundations and create repeatable pathways from experimentation to production — moving beyond “What can AI do?” toward “Where can AI create durable enterprise value, and what does it take to scale it?”

Systems that plan, use tools and complete multi-step workflows within defined boundaries.

Traditional automation follows predefined rules. Generative AI can produce content, interpret information and assist employees. Agentic AI introduces another layer: systems that pursue a defined objective by planning actions, using tools, interacting with systems and completing multi-step workflows within specified boundaries.

For financial services, that could transform onboarding, customer servicing, fraud investigation, claims processing, compliance reviews, loan workflows and relationship-manager support. The Cambridge Centre for Alternative Finance's 2026 Global AI in Financial Services Report found that 52% of surveyed industry respondents were already actively adopting agentic AI, while 81% expected meaningful agentic AI adoption by 2030.

But autonomy creates a new question: how much authority should an AI system have? FINOVAI 2027 will explore the practical architecture behind agentic AI — permissions, human oversight, auditability, identity, model orchestration, workflow integration, risk controls and the boundaries between autonomous action and human decision-making.

Beyond the chatbot — natural language connected to real financial workflows.

Customers increasingly expect to interact with technology through natural language rather than navigating complex menus, searching help centres or repeating information across channels.

The next generation of conversational AI can understand context, interpret intent, retrieve information, personalise interactions and connect conversations to underlying workflows. Combined with agentic capabilities, it can move from answering a customer's question to helping complete the task behind it.

Imagine a customer asking, “Can I afford this mortgage?” The future experience could understand the customer's financial context, explain relevant options, identify missing information, simulate scenarios and guide the customer through the next step. FINOVAI 2027 will examine how banks deploy conversational AI across customer service, contact centres, digital banking, financial guidance, onboarding and relationship management while maintaining accuracy, security and human escalation.

The next advantage is not faster journeys. It is smarter ones.

The future of customer experience is increasingly about context. Customers do not want to explain who they are, what they need and what happened previously every time they interact with a financial institution. They expect systems to understand the journey.

Conversational AI can provide the natural interface. Predictive intelligence can identify needs. Agentic AI can orchestrate actions. Human employees step in when judgement, empathy or complex decision-making is required — creating a more continuous experience that does not reset every time a customer changes channel.

But personalisation also creates responsibility. The more intelligent the experience becomes, the more important transparency, privacy and control become. FINOVAI 2027 will explore how institutions create AI-powered customer experience strategies that are not only faster and more personalised, but also trusted.

Governance as an enabler of responsible innovation, not a barrier to it.

As AI moves into more consequential workflows, financial institutions must understand not only whether a model works, but whether it can be governed throughout its lifecycle. That means explainability, data quality, model risk, cybersecurity, third-party dependencies, operational resilience, accountability and human oversight.

The ECB has specifically highlighted gaps banks still need to address, including AI-specific risk management, data-quality controls, explainability, full lifecycle model governance and clear accountability for AI-driven decisions.

The challenge becomes more complex with agentic AI, because autonomous systems can interact with multiple tools and systems and potentially take actions rather than simply provide recommendations. FINOVAI 2027 brings technology, risk, compliance and business leaders into the same conversation.

Detect faster. Decide smarter. Protect customers without unnecessary friction.

Deepfakes, synthetic identities, automated social engineering, sophisticated phishing and AI-assisted cyberattacks are changing the threat landscape.

The European Banking Authority's 2026 Risk Assessment Report identifies AI-related cyber risks as a growing concern, and highlights the transformative potential of AI for efficiency, process automation, risk management, financial advisory, customer service and fraud detection.

FINOVAI 2027 will explore the next generation of AI-powered fraud prevention, including behavioural analytics, real-time detection, synthetic identity protection, financial crime investigation and the use of AI against AI-enabled threats.

From isolated AI tools to connected, intelligent insurance workflows.

AI can process documents, interpret unstructured information, support underwriting decisions, identify suspicious claims, assist brokers and improve customer interactions. The next opportunity is connecting these capabilities.

Instead of isolated AI tools, insurers can create connected workflows in which agentic AI helps coordinate multiple steps while people retain control over important decisions. That could turn claims journeys from fragmented processes into more continuous experiences, help underwriters access relevant information faster and give service teams more context on complex cases.

FINOVAI 2027 will examine how insurers move beyond individual AI applications toward connected workflows that improve efficiency, risk management and customer experience.

Performance alone is not enough when AI influences financially significant decisions.

Financial institutions have always relied on models. AI changes the scale, complexity and potential reach of those models.

When AI influences credit, fraud, risk or other financially significant decisions, institutions need to understand how systems behave, where they can fail and how decisions can be challenged or explained. The European Parliament has highlighted the increasing use of AI in financial services, including creditworthiness assessment, and stresses the importance of human oversight for autonomous AI systems.

The future of AI in banking will depend not only on building models that perform well. It will depend on building models that institutions can trust, explain and govern.

The question is not which jobs go. It is how work itself gets redesigned.

Relationship managers can use AI to prepare for customer conversations. Analysts can delegate information gathering and synthesis. Operations teams can automate repetitive workflows. Customer-service professionals can receive real-time assistance. Risk teams can prioritise investigations more intelligently.

Lloyds Banking Group, for example, describes agentic AI applications across customer interactions, back-office operations, frontline support and colleague enablement, with the objective of allowing employees to focus more heavily on higher-value work.

FINOVAI 2027 will explore what this means for leadership, skills, productivity, organisational design and employee trust. The objective is not to automate people out of processes. It is to determine what humans should do better when AI does more of the work around them.

An AI strategy is only as strong as the infrastructure supporting it.

Banks and insurers must consider where data resides, which models they use, how systems connect, how third-party providers are managed and how resilient critical AI capabilities remain during outages or attacks.

This matters increasingly as financial institutions depend on a relatively concentrated ecosystem of cloud, infrastructure and AI providers. Recent analysis has highlighted the systemic and operational risks that can arise from growing dependence on major technology providers.

For European financial services, sovereignty adds another strategic dimension: how can institutions innovate quickly while maintaining control over sensitive data, critical systems and strategic AI capabilities? FINOVAI 2027 examines secure architecture, data residency, model strategy, cybersecurity, third-party risk and operational resilience.

From “we are experimenting with AI” to “here is the value, and here is how we measure it”.

Boards and executive teams increasingly need to know what AI investments are actually producing. Is AI increasing revenue? Reducing operational costs? Improving employee productivity? Preventing fraud? Improving customer retention? Reducing turnaround time? Creating a better customer experience?

These questions are more important than the number of AI pilots an institution has launched.

FINOVAI 2027 will focus on the metrics that matter and the operating models required to connect AI initiatives to measurable outcomes.